• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.25-28
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• 2007

Electroencephalograhic(EEG)는 심리학의 영역에서 인간 두뇌의 활동을 측정 기록하는데 오래 전부터 사용하였다. 과학의 발달함에 따라 점차적으로 인간의 두뇌에서 감정을 조절하는 기본적인 영역들이 밝혀지고 있다. 그래서 인간의 감정을 조절하는 인간의 두뇌 활동 영역들을 EEG를 이용하여 측정하였다. 본 논문에서는 EEG의 신호들과 몸짓을 이용해서 감정을 인식하였다. 특히, 기존에 생체신호나 몸짓 중 한 가지만을 이용하여 각각 실험해서 감성을 인식하였지만, 본 논문에서는 EEG 신호와 몸짓을 동시에 이용해서 피 실험자의 감성을 인식하는 실험을 하였다. 실험결과 기존의 생체신호나 몸짓 한 가지만을 가지고 실험했을 때의 인식률 보다 더 높은 인식률을 보임을 알 수 있었다. 그리고 생체신호와 몸짓들의 특징 신호들은 강화학습의 개념을 이용한 IFS(Interactive Feature Selection)를 이용하여 특징 선택을 하였다.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.19-26
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• 2005

This paper introduces a new design method of realizing the machine control interface by using bio-signals(EEG/EOG). This method can be further expanded to be applied to the computer system responding to EEG or EOG signals and the general bio-feedback system. For this reason, we made the remotely controlled toy system controlled by the EEG spectrums, their combination indexes, and EOG parameters. And the headset that has bio-signal processing modules built-in offers convenience for users, and this make much more advanced system than any other existing BCI and BMI system.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.465-471
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• 2018

Recently, biometric technology has begun to be used as a fusion of IT technology and financial system. Using this biometric technology, FIDO(Fast Identity Online) technology, Samsung and Apple started Samsung Pay and Apple Pay service. FIDO authentication technology replaces existing authentication methods such as passwords. Among the biometric technologies, fingerprint recognition technology is attracting attention because it can minimize the device and user rejection at a relatively low price. However, fingerprint information has a limited number of users and it can not be reused if fingerprint information is leaked by an external attacker. Therefore, in this paper, we propose a method to authenticate a user using EEG signal which is one of biometrics technologies. W propose a method to use EEG signal measurement value in FIDO system by using convenience channel by using short channel EEG device. And propose a method to utilize EEG signal when the user recognizes a specific entity by measuring the EEG signal before and after recognizing a specific entity.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.322-327
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• 2007

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study emotion recognition method which uses one of physiological signals and gestures in the existing research. In this paper, we use together physiological signals and gestures for emotion recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1179-1187
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• 2023

Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.105-114
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• 2015

We propose a real time user interface that utilizes emotion recognition by physiological signals. To improve the problem that was low accuracy of emotion recognition through the traditional EEG(ElectroEncephaloGram), We developed a physiological signals-based emotion recognition system mixing relative power spectrum values of theta/alpha/beta/gamma EEG waves and autonomic nerve signal ratio of ECG (ElectroCardioGram). We propose both a data map and weight value modification algorithm to recognize six emotions of happy, fear, sad, joy, anger, and hatred. The datamap that stores the user-specific probability value is created and the algorithm updates the weighting to improve the accuracy of emotion recognition corresponding to each EEG channel. Also, as we compared the results of the EEG/ECG bio-singal complex data and single data consisting of EEG, the accuracy went up 23.77%. The proposed interface system with high accuracy will be utillized as a useful interface for controlling the game spaces and smart spaces.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.89-95
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• 2015

Compresses sensing (CS) technique is beneficial for reducing power consumption of biopotential acquisition circuits in wireless healthcare system. This paper investigates the maximum possible compress ratio for various biopotential signal when the CS technique is applied. By using the CS technique, we perform the compression and reconstruction of typical electrocardiogram(ECG), electromyogram(EMG), electroencephalogram(EEG) signals. By comparing the original signal and reconstructed signal, we determines the validity of the CS-based signal compression. Raw-biopotential signal is compressed by using a psuedo-random matrix, and the compressed signal is reconstructed by using the Block Sparse Bayesian Learning(BSBL) algorithm. EMG signal, which is the most sparse biopotential signal, the maximum compress ratio is found to be 10, and the ECG'sl maximum compress ratio is found to be 5. EEG signal, which is the least sparse bioptential signal, the maximum compress ratio is found to be 4. The results of this work is useful and instrumental for the design of wireless biopotential signal monitoring circuits.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.179-182
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• 2001

인체 활동에 따라 우리 몸에는 다양한 전기적 생체신호가 발생하며 특히 뇌의 활동에 따라 발생되는 뇌파(EEG)는 비침습적 방법으로 측정될 수 있는 장점 때문에 뇌기능 연구 및 임상 등에서 널리 사용되어지고 있다. 또한 임상에서는 주로 뇌 신경계 질환환자의 병인 규명 및 기전 연구를 위하여 뇌파가 사용되어지고 있다. 최근에는 컴퓨터 발달에 따라 카오스, 비선형 이론 등의 다양한 방법으로 복잡한 시계열 신호인 뇌파를 분석하는 기법들이 개발되어 뇌파의 특징점을 찾아 임상에 활용하거나 뇌기능 연구에 적용하려는 연구가 진행되고 있다. 본 논문에서는 잡화(artifact)가 섞여 있는 뇌파신호 및 artifact가 제거된 다음 재구성된 뇌파신호(reconstructed EEG signal), 그리고 독립성분으로 분리된 각각의 신호에 대하여 특징점을 찾기 위하여 비선형 및 선형 분석을 실시하여 유의한 차이점을 밝혔다.